Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f69c72d0b38>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f69c38b13c8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
/home/ubuntu/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
TensorFlow Version: 1.8.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, (None), name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer from celeba is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        lrelu1 = tf.maximum(alpha * x1, x1)
        # now 14x14x64
        x2 = tf.layers.conv2d(lrelu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        lrelu2 = tf.maximum(alpha * bn2, bn2)
        # now 7x7x128
        x3 = tf.layers.conv2d(lrelu2, 265, 5, strides=1, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        lrelu3 = tf.maximum(alpha * bn3, bn3)
        # now 7x7x265
        x4 = tf.layers.conv2d(lrelu3, 512, 5, strides=1, padding='same')
        bn4 = tf.layers.batch_normalization(x4, training=True)
        lrelu4 = tf.maximum(alpha * bn4, bn4)
        # now 7x7x512
                
        flat = tf.reshape(lrelu4, (-1, 7*7*512))
        
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    reuse = True
    if(is_train):
        reuse=False
    
    # Implement Function
    with tf.variable_scope('generator', reuse=reuse):
        # Fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        bn1 = tf.layers.batch_normalization(x1, training=is_train)
        lrelu1 = tf.maximum(alpha * bn1, bn1)
        # now 7x7x512
        
        # Convolutional layers
        x2 = tf.layers.conv2d_transpose(lrelu1, 256, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=is_train)
        lrelu2 = tf.maximum(alpha * bn2, bn2)
        #now 14x14x265
        
        x3 = tf.layers.conv2d_transpose(lrelu2, 128, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=is_train)
        lrelu3 = tf.maximum(alpha * bn3, bn3)
        #now 28x28x128
        
        x4 = tf.layers.conv2d_transpose(lrelu3, 64, 5, strides=1, padding='same')
        bn4 = tf.layers.batch_normalization(x4, training=is_train)
        lrelu4 = tf.maximum(alpha * bn4, bn4)
        #now 28x28x64
        
        logits = tf.layers.conv2d_transpose(lrelu4, out_channel_dim, 5, strides=1, padding='same')
        # 28x28xout_channel_dim
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels = tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels = tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels = tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Implement Function
    t_vars = tf.trainable_variables()
    
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    # From dcgan-svhn
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """    
    # Build Model
    sample_z = np.random.uniform(-1, 1, size=(72, z_dim)) # For sampling
    steps = 0
    
    # print(data_shape) with MNIST: (60000, 28, 28, 1)
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
        
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                #print(batch_images.shape) # MNIST: 128, 28, 28, 1
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim)) # Get random noise
                
                # Optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                # Printing losses - from dcgan-svhn
                if steps % 50 == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images, lr: learning_rate})
                    train_loss_g = g_loss.eval({input_z: batch_z, lr: learning_rate})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Steps {}".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                    # Showing examples
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)    

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 64
z_dim = 100
learning_rate = 0.0003
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Steps 50 Discriminator Loss: 5.7420... Generator Loss: 0.2591
Epoch 1/2... Steps 100 Discriminator Loss: 2.6294... Generator Loss: 0.2379
Epoch 1/2... Steps 150 Discriminator Loss: 0.9426... Generator Loss: 1.0423
Epoch 1/2... Steps 200 Discriminator Loss: 0.5943... Generator Loss: 4.3120
Epoch 1/2... Steps 250 Discriminator Loss: 0.8243... Generator Loss: 1.0971
Epoch 1/2... Steps 300 Discriminator Loss: 0.6956... Generator Loss: 1.2400
Epoch 1/2... Steps 350 Discriminator Loss: 1.2147... Generator Loss: 10.2342
Epoch 1/2... Steps 400 Discriminator Loss: 0.4306... Generator Loss: 1.7313
Epoch 1/2... Steps 450 Discriminator Loss: 0.1440... Generator Loss: 2.9593
Epoch 1/2... Steps 500 Discriminator Loss: 1.2587... Generator Loss: 0.6875
Epoch 1/2... Steps 550 Discriminator Loss: 1.5044... Generator Loss: 3.0568
Epoch 1/2... Steps 600 Discriminator Loss: 1.4045... Generator Loss: 3.8987
Epoch 1/2... Steps 650 Discriminator Loss: 0.3067... Generator Loss: 2.3184
Epoch 1/2... Steps 700 Discriminator Loss: 0.0060... Generator Loss: 6.9506
Epoch 1/2... Steps 750 Discriminator Loss: 0.9521... Generator Loss: 4.0748
Epoch 1/2... Steps 800 Discriminator Loss: 0.3924... Generator Loss: 3.9539
Epoch 1/2... Steps 850 Discriminator Loss: 0.0245... Generator Loss: 5.5552
Epoch 1/2... Steps 900 Discriminator Loss: 0.6815... Generator Loss: 1.3346
Epoch 2/2... Steps 950 Discriminator Loss: 0.3775... Generator Loss: 1.6286
Epoch 2/2... Steps 1000 Discriminator Loss: 0.2831... Generator Loss: 9.3834
Epoch 2/2... Steps 1050 Discriminator Loss: 0.0166... Generator Loss: 6.3758
Epoch 2/2... Steps 1100 Discriminator Loss: 0.0127... Generator Loss: 7.1515
Epoch 2/2... Steps 1150 Discriminator Loss: 0.5896... Generator Loss: 1.5037
Epoch 2/2... Steps 1200 Discriminator Loss: 0.4857... Generator Loss: 1.7863
Epoch 2/2... Steps 1250 Discriminator Loss: 0.8301... Generator Loss: 10.6246
Epoch 2/2... Steps 1300 Discriminator Loss: 0.7442... Generator Loss: 1.3951
Epoch 2/2... Steps 1350 Discriminator Loss: 1.3017... Generator Loss: 0.6200
Epoch 2/2... Steps 1400 Discriminator Loss: 0.0034... Generator Loss: 6.7761
Epoch 2/2... Steps 1450 Discriminator Loss: 0.1707... Generator Loss: 2.4089
Epoch 2/2... Steps 1500 Discriminator Loss: 0.0095... Generator Loss: 9.8979
Epoch 2/2... Steps 1550 Discriminator Loss: 0.0015... Generator Loss: 12.6872
Epoch 2/2... Steps 1600 Discriminator Loss: 0.0016... Generator Loss: 13.2047
Epoch 2/2... Steps 1650 Discriminator Loss: 3.7967... Generator Loss: 0.0421
Epoch 2/2... Steps 1700 Discriminator Loss: 0.0117... Generator Loss: 5.8118
Epoch 2/2... Steps 1750 Discriminator Loss: 0.1014... Generator Loss: 3.1029
Epoch 2/2... Steps 1800 Discriminator Loss: 0.0185... Generator Loss: 5.8384
Epoch 2/2... Steps 1850 Discriminator Loss: 0.8166... Generator Loss: 6.9679

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 64
z_dim = 100
learning_rate = 0.0003
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2... Steps 50 Discriminator Loss: 0.6581... Generator Loss: 1.7730
Epoch 1/2... Steps 100 Discriminator Loss: 3.5267... Generator Loss: 13.0645
Epoch 1/2... Steps 150 Discriminator Loss: 0.4789... Generator Loss: 1.5431
Epoch 1/2... Steps 200 Discriminator Loss: 1.1490... Generator Loss: 0.8130
Epoch 1/2... Steps 250 Discriminator Loss: 0.7369... Generator Loss: 3.1840
Epoch 1/2... Steps 300 Discriminator Loss: 1.6070... Generator Loss: 5.6940
Epoch 1/2... Steps 350 Discriminator Loss: 0.4613... Generator Loss: 2.4856
Epoch 1/2... Steps 400 Discriminator Loss: 0.1672... Generator Loss: 2.8775
Epoch 1/2... Steps 450 Discriminator Loss: 0.1790... Generator Loss: 7.2889
Epoch 1/2... Steps 500 Discriminator Loss: 1.1588... Generator Loss: 2.5083
Epoch 1/2... Steps 550 Discriminator Loss: 1.0697... Generator Loss: 0.4985
Epoch 1/2... Steps 600 Discriminator Loss: 0.9565... Generator Loss: 2.6011
Epoch 1/2... Steps 650 Discriminator Loss: 0.8481... Generator Loss: 2.8228
Epoch 1/2... Steps 700 Discriminator Loss: 1.0409... Generator Loss: 0.6993
Epoch 1/2... Steps 750 Discriminator Loss: 0.3123... Generator Loss: 1.7723
Epoch 1/2... Steps 800 Discriminator Loss: 0.8225... Generator Loss: 1.0180
Epoch 1/2... Steps 850 Discriminator Loss: 0.0367... Generator Loss: 7.0110
Epoch 1/2... Steps 900 Discriminator Loss: 1.3869... Generator Loss: 0.7649
Epoch 1/2... Steps 950 Discriminator Loss: 1.1873... Generator Loss: 0.9982
Epoch 1/2... Steps 1000 Discriminator Loss: 1.3057... Generator Loss: 0.8369
Epoch 1/2... Steps 1050 Discriminator Loss: 2.0596... Generator Loss: 0.2666
Epoch 1/2... Steps 1100 Discriminator Loss: 0.4515... Generator Loss: 1.8155
Epoch 1/2... Steps 1150 Discriminator Loss: 1.7113... Generator Loss: 0.2910
Epoch 1/2... Steps 1200 Discriminator Loss: 2.1173... Generator Loss: 0.7369
Epoch 1/2... Steps 1250 Discriminator Loss: 1.1391... Generator Loss: 0.6528
Epoch 1/2... Steps 1300 Discriminator Loss: 1.9824... Generator Loss: 0.1929
Epoch 1/2... Steps 1350 Discriminator Loss: 0.5797... Generator Loss: 1.7968
Epoch 1/2... Steps 1400 Discriminator Loss: 1.2117... Generator Loss: 0.7953
Epoch 1/2... Steps 1450 Discriminator Loss: 1.1449... Generator Loss: 1.1701
Epoch 1/2... Steps 1500 Discriminator Loss: 1.2369... Generator Loss: 0.7993
Epoch 1/2... Steps 1550 Discriminator Loss: 1.2813... Generator Loss: 0.7637
Epoch 1/2... Steps 1600 Discriminator Loss: 1.3323... Generator Loss: 1.4882
Epoch 1/2... Steps 1650 Discriminator Loss: 1.0251... Generator Loss: 1.2306
Epoch 1/2... Steps 1700 Discriminator Loss: 1.2061... Generator Loss: 0.8960
Epoch 1/2... Steps 1750 Discriminator Loss: 0.9752... Generator Loss: 0.7112
Epoch 1/2... Steps 1800 Discriminator Loss: 1.3427... Generator Loss: 0.6582
Epoch 1/2... Steps 1850 Discriminator Loss: 1.6000... Generator Loss: 0.3381
Epoch 1/2... Steps 1900 Discriminator Loss: 1.3758... Generator Loss: 0.4315
Epoch 1/2... Steps 1950 Discriminator Loss: 1.3523... Generator Loss: 0.5038
Epoch 1/2... Steps 2000 Discriminator Loss: 1.3885... Generator Loss: 0.4894
Epoch 1/2... Steps 2050 Discriminator Loss: 1.2818... Generator Loss: 1.7528
Epoch 1/2... Steps 2100 Discriminator Loss: 1.1957... Generator Loss: 0.5729
Epoch 1/2... Steps 2150 Discriminator Loss: 1.3661... Generator Loss: 1.6920
Epoch 1/2... Steps 2200 Discriminator Loss: 1.1042... Generator Loss: 0.8251
Epoch 1/2... Steps 2250 Discriminator Loss: 0.4318... Generator Loss: 2.0546
Epoch 1/2... Steps 2300 Discriminator Loss: 0.1999... Generator Loss: 2.9650
Epoch 1/2... Steps 2350 Discriminator Loss: 1.1777... Generator Loss: 3.0678
Epoch 1/2... Steps 2400 Discriminator Loss: 0.0422... Generator Loss: 5.1507
Epoch 1/2... Steps 2450 Discriminator Loss: 2.2568... Generator Loss: 5.0983
Epoch 1/2... Steps 2500 Discriminator Loss: 0.0113... Generator Loss: 8.4013
Epoch 1/2... Steps 2550 Discriminator Loss: 1.2323... Generator Loss: 0.7204
Epoch 1/2... Steps 2600 Discriminator Loss: 1.2236... Generator Loss: 0.9577
Epoch 1/2... Steps 2650 Discriminator Loss: 1.4947... Generator Loss: 0.4943
Epoch 1/2... Steps 2700 Discriminator Loss: 1.0538... Generator Loss: 1.7013
Epoch 1/2... Steps 2750 Discriminator Loss: 1.8188... Generator Loss: 0.2406
Epoch 1/2... Steps 2800 Discriminator Loss: 1.4794... Generator Loss: 0.5034
Epoch 1/2... Steps 2850 Discriminator Loss: 1.3642... Generator Loss: 0.5922
Epoch 1/2... Steps 2900 Discriminator Loss: 1.1525... Generator Loss: 1.5195
Epoch 1/2... Steps 2950 Discriminator Loss: 2.0093... Generator Loss: 0.2117
Epoch 1/2... Steps 3000 Discriminator Loss: 1.2761... Generator Loss: 0.5436
Epoch 1/2... Steps 3050 Discriminator Loss: 1.2891... Generator Loss: 0.5828
Epoch 1/2... Steps 3100 Discriminator Loss: 1.4519... Generator Loss: 0.5944
Epoch 1/2... Steps 3150 Discriminator Loss: 1.5287... Generator Loss: 0.3419
Epoch 2/2... Steps 3200 Discriminator Loss: 0.9566... Generator Loss: 1.3765
Epoch 2/2... Steps 3250 Discriminator Loss: 1.0759... Generator Loss: 0.9035
Epoch 2/2... Steps 3300 Discriminator Loss: 1.6566... Generator Loss: 0.3118
Epoch 2/2... Steps 3350 Discriminator Loss: 0.9365... Generator Loss: 1.0983
Epoch 2/2... Steps 3400 Discriminator Loss: 1.4495... Generator Loss: 1.3937
Epoch 2/2... Steps 3450 Discriminator Loss: 0.7449... Generator Loss: 1.6314
Epoch 2/2... Steps 3500 Discriminator Loss: 0.5364... Generator Loss: 1.6388
Epoch 2/2... Steps 3550 Discriminator Loss: 0.0978... Generator Loss: 5.3334
Epoch 2/2... Steps 3600 Discriminator Loss: 0.3913... Generator Loss: 3.1343
Epoch 2/2... Steps 3650 Discriminator Loss: 0.8896... Generator Loss: 1.3168
Epoch 2/2... Steps 3700 Discriminator Loss: 0.5063... Generator Loss: 5.4213
Epoch 2/2... Steps 3750 Discriminator Loss: 3.0244... Generator Loss: 0.0987
Epoch 2/2... Steps 3800 Discriminator Loss: 1.6453... Generator Loss: 0.3710
Epoch 2/2... Steps 3850 Discriminator Loss: 0.0821... Generator Loss: 3.7248
Epoch 2/2... Steps 3900 Discriminator Loss: 0.6344... Generator Loss: 1.0356
Epoch 2/2... Steps 3950 Discriminator Loss: 0.4720... Generator Loss: 1.6484
Epoch 2/2... Steps 4000 Discriminator Loss: 2.0665... Generator Loss: 0.2297
Epoch 2/2... Steps 4050 Discriminator Loss: 0.2348... Generator Loss: 5.2062
Epoch 2/2... Steps 4100 Discriminator Loss: 1.3253... Generator Loss: 3.6619
Epoch 2/2... Steps 4150 Discriminator Loss: 0.1094... Generator Loss: 2.7724
Epoch 2/2... Steps 4200 Discriminator Loss: 0.7399... Generator Loss: 1.8853
Epoch 2/2... Steps 4250 Discriminator Loss: 1.0671... Generator Loss: 0.6973
Epoch 2/2... Steps 4300 Discriminator Loss: 0.0105... Generator Loss: 8.6040
Epoch 2/2... Steps 4350 Discriminator Loss: 0.0851... Generator Loss: 4.0317
Epoch 2/2... Steps 4400 Discriminator Loss: 0.0302... Generator Loss: 5.2987
Epoch 2/2... Steps 4450 Discriminator Loss: 0.4622... Generator Loss: 1.9796
Epoch 2/2... Steps 4500 Discriminator Loss: 0.6164... Generator Loss: 6.4962
Epoch 2/2... Steps 4550 Discriminator Loss: 0.1221... Generator Loss: 2.7529
Epoch 2/2... Steps 4600 Discriminator Loss: 1.5809... Generator Loss: 0.8510
Epoch 2/2... Steps 4650 Discriminator Loss: 1.4119... Generator Loss: 0.9419
Epoch 2/2... Steps 4700 Discriminator Loss: 0.9652... Generator Loss: 1.1624
Epoch 2/2... Steps 4750 Discriminator Loss: 1.1919... Generator Loss: 0.9735
Epoch 2/2... Steps 4800 Discriminator Loss: 1.7572... Generator Loss: 0.3130
Epoch 2/2... Steps 4850 Discriminator Loss: 1.5141... Generator Loss: 0.5832
Epoch 2/2... Steps 4900 Discriminator Loss: 1.3083... Generator Loss: 0.9507
Epoch 2/2... Steps 4950 Discriminator Loss: 1.4721... Generator Loss: 0.4308
Epoch 2/2... Steps 5000 Discriminator Loss: 1.1678... Generator Loss: 0.9327
Epoch 2/2... Steps 5050 Discriminator Loss: 1.1004... Generator Loss: 0.8105
Epoch 2/2... Steps 5100 Discriminator Loss: 1.4724... Generator Loss: 0.4092
Epoch 2/2... Steps 5150 Discriminator Loss: 0.8880... Generator Loss: 1.1508
Epoch 2/2... Steps 5200 Discriminator Loss: 0.8051... Generator Loss: 1.3765
Epoch 2/2... Steps 5250 Discriminator Loss: 0.2124... Generator Loss: 2.4066
Epoch 2/2... Steps 5300 Discriminator Loss: 0.0398... Generator Loss: 6.1045
Epoch 2/2... Steps 5350 Discriminator Loss: 0.5137... Generator Loss: 2.2446
Epoch 2/2... Steps 5400 Discriminator Loss: 1.7521... Generator Loss: 0.4504
Epoch 2/2... Steps 5450 Discriminator Loss: 1.3080... Generator Loss: 0.6048
Epoch 2/2... Steps 5500 Discriminator Loss: 1.3975... Generator Loss: 0.6989
Epoch 2/2... Steps 5550 Discriminator Loss: 1.1515... Generator Loss: 0.8213
Epoch 2/2... Steps 5600 Discriminator Loss: 2.1861... Generator Loss: 0.1936
Epoch 2/2... Steps 5650 Discriminator Loss: 1.2835... Generator Loss: 0.8200
Epoch 2/2... Steps 5700 Discriminator Loss: 0.3128... Generator Loss: 1.9893
Epoch 2/2... Steps 5750 Discriminator Loss: 0.3590... Generator Loss: 2.5561
Epoch 2/2... Steps 5800 Discriminator Loss: 0.2296... Generator Loss: 2.6533
Epoch 2/2... Steps 5850 Discriminator Loss: 0.0858... Generator Loss: 4.5743
Epoch 2/2... Steps 5900 Discriminator Loss: 0.5939... Generator Loss: 1.4324
Epoch 2/2... Steps 5950 Discriminator Loss: 0.6919... Generator Loss: 1.3919
Epoch 2/2... Steps 6000 Discriminator Loss: 1.2076... Generator Loss: 3.5478
Epoch 2/2... Steps 6050 Discriminator Loss: 1.6800... Generator Loss: 0.3265
Epoch 2/2... Steps 6100 Discriminator Loss: 1.1779... Generator Loss: 0.7194
Epoch 2/2... Steps 6150 Discriminator Loss: 1.3817... Generator Loss: 1.0019
Epoch 2/2... Steps 6200 Discriminator Loss: 1.1331... Generator Loss: 0.8762
Epoch 2/2... Steps 6250 Discriminator Loss: 1.3795... Generator Loss: 0.5737
Epoch 2/2... Steps 6300 Discriminator Loss: 1.1536... Generator Loss: 0.7739

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.